A New Framework for Resource Allocation in Wireless Sensor Networks Using Machine Learning Techniques

this paper affords a brand new clever aid allocation framework for Wi-Fi sensor networks through the use of system learning strategies. The proposed framework learns the underlying dynamic of the community by leveraging the external statistics received from its acquaintances and the usage to allocate the to-be-had sources effectively. For instance, it is able to identify and classify exceptional sorts of indicators, determine the most effective transmission links, and successfully manipulate interference arising because of a couple of transmission hyperlinks. Several experiments have been carried out on various actual global information units with distinct topologies and channel situations. The consequences display that the proposed framework appreciably outperforms the present allocation algorithms. Furthermore, the proposed framework is resilient to community failures and might quickly adapt to modifications in community situations. The paper offers a new shrewd aid allocation framework for wireless sensor networks using gadget studying techniques. The proposed framework learns the underlying dynamic of the network by means of leveraging the external statistics acquired from its neighbors. The proposed framework can pick out and classify one-of-a-kind types of indicators, decide the maximum effective transmission hyperlinks, and control interference arising because of a couple of transmission links. Experiments screen that the proposed framework appreciably outperforms the prevailing allocation algorithms and is resilient to community disasters.